import scipp as sc
import mantid.simpleapi as manty
import plotly.graph_objs as go
import numpy as np
from scipp import Dim
d = sc.Dataset(
....: {'a': sc.Variable(dims=[Dim.X, Dim.Y], values=np.random.rand(1000, 1000)),
....: 'b': sc.Variable(1.0)},
....: coords={
....: Dim.X: sc.Variable([Dim.X], values=np.arange(1000.0), unit=sc.units.m),
....: Dim.Y: sc.Variable([Dim.Y], values=np.arange(1000.0), unit=sc.units.us)},
....: labels={
....: 'aux': sc.Variable([Dim.Y], values=np.random.rand(1000))})
print(d)
sc.plot(d)
<scipp.Dataset>
Dimensions: {{Dim.Y, 1000}, {Dim.X, 1000}}
Coordinates:
Dim.X double [m] (Dim.X)
Dim.Y double [μs] (Dim.Y)
Labels:
aux double [dimensionless] (Dim.Y)
Data:
a double [dimensionless] (Dim.X, Dim.Y)
b double [dimensionless] ()
Attributes:
data = manty.Load('/home/jon/Sandbox/Data/MAR19717.raw')
def workspace2dToDataSet(workspace):
signal = mtd[workspace].extractY()
error = mtd[workspace].extractE()
TOF = mtd[workspace].extractX()[0]
wkspDim=signal.shape
TOFAxis=(TOF[0:wkspDim[1]])
d = sc.Dataset(
{'data': sc.Variable(dims=[Dim.X, Dim.Y], values=signal),
},
coords={
Dim.X: sc.Variable([Dim.X], values=np.arange(signal.shape[0]), unit=sc.units.m),
Dim.Y: sc.Variable([Dim.Y], values=TOFAxis, unit=sc.units.us)},
)
print(d)
return d
d = workspace2dToDataSet('data')
<scipp.Dataset>
Dimensions: {{Dim.Y, 1900}, {Dim.X, 922}}
Coordinates:
Dim.X int64 [m] (Dim.X)
Dim.Y double [μs] (Dim.Y)
Labels:
Data:
data double [dimensionless] (Dim.X, Dim.Y)
Attributes:
sc.plot(d)
import ipywidgets as ipw
mantidList=(dir(manty))
from mantid.simpleapi import *
algorithum_list = ipw.Dropdown(
options = mantidList,
description='Mantid Simple API algorithums',
disabled=False,
)
helpButton=ipw.Button(value=False,
description='MantidHelp',
disabled=False,
button_style='info', # 'success', 'info', 'warning', 'danger' or ''
tooltip='Description',
icon='?')
display(algorithum_list)
display(helpButton)
out = ipw.Output(layout={'border': '1px solid black'})
display(out)
@out.capture()
def on_button_clicked(b):
help(globals()[algorithum_list.get_interact_value()])
helpButton.on_click(on_button_clicked)
qtigenie is a sript that has the iliad function that will convert neutron TOF data to energy transfer data
This converts the units of Time of Flight in micro seconds to Energy Transfer in meV
import sys
sys.path.append('/home/jon/Sandbox/Scripts/qtiGenie/')
sys.path.append('/home/jon/Sandbox/Scripts/MARI/')
sys.path.append('/home/jon/Sandbox/Scripts/MARI/MapFiles/')
pathToData='/home/jon/Sandbox/Data/'
from qtiGenie import iliad
import ipywidgets as widgets
Using qtiplot graphics You can change it by issuing setinst(InstrumentName) command where InstrumentName can be MER, MAR, MAP, LET, TSK or XSD
from Direct.DirectEnergyConversion import *
import Direct.dgreduce as dgreduce
iliad_setup=dgreduce.setup
iliad=dgreduce.arb_units
iliad_abs=dgreduce.abs_units
iliad_reducer = dgreduce.getReducer
#@out.capture()
def reduceSimple(runNumber):
inst='mar'
iliad_setup(inst)
ext='.raw'
#mapfile='mari_res2013'
mapfile='/home/jon/Sandbox/Scripts/MARI/MapFiles/mari_121_cor'
#det_cal_file must be specified if the reduction sends out put to a workpsace
cal_file=pathToData+'MAR19717.raw'
#hard mask file
#mask_file='/home/mari/Users/taylor/mask.msk'
#load vanadium file
whitebeamfile=pathToData+'MAR19717.raw'
LoadRaw(Filename=whitebeamfile,OutputWorkspace="wb_wksp",LoadLogFiles="0")
runs=pathToData+'MAR'+str(runNumber)+'.raw'; ei=12; rebin_params='-10,.05,11'; sum=False
LoadRaw(Filename=runs,OutputWorkspace="run_wksp",LoadLogFiles="0")
w1=iliad("wb_wksp","run_wksp",ei,rebin_params,mapfile,fixei=False,det_cal_file=cal_file,norm_method='current')
We could call reduce simple in a cell Instead we can make a small UI to run it for us using a widget and sending the output to a text widget with @capture
button1 = widgets.Button(description="Reduce Data")
display(button1)
button2 = widgets.Button(description="Clear")
display(button2)
text_field=widgets.Text(value='run number')
display(text_field)
TextOutput = widgets.Output(layout={'border': '1px solid black'})
display(TextOutput)
@TextOutput.capture()
def on_button_clicked(b):
runNumber = text_field.value
print ('Running Iliad on file ',runNumber)
reduceSimple(runNumber)
ei = mtd['w1'].getRun().getLogData("Ei").value
print('Incident Neutron Energy for run', runNumber,' determined as ',ei,'meV')
@TextOutput.capture()
def on_clear_clicked(b):
TextOutput.clear_output()
button1.on_click(on_button_clicked)
button2.on_click(on_clear_clicked)
The mantid workspace has all the instrument & run meta data
incident_energy=mtd['w1'].getRun().getLogData("Ei").value
print (incident_energy,'meV')
12.045315866660905 meV
%matplotlib inline
import matplotlib.pyplot as plt
mtd.importAll()
workSpaceList = %who_ls Workspace2D
workSpaceListWidget = widgets.Dropdown(
options=workSpaceList,
description='WorkSpace:',
disabled=False,
)
buttonSelectWorkspace = widgets.Button(description="Select Workspace",layout=widgets.Layout(width='auto', grid_area='main'))
#display(buttonPlotSpectrum)
buttonPlotSpectrum = widgets.Button(description="plot spectrum",layout=widgets.Layout(width='auto', grid_area='main'))
#display(buttonPlotSpectrum)
buttonPlot2D = widgets.Button(description="plot 2D",layout=widgets.Layout(width='auto', grid_area='main'))
#display(buttonPlot2D)
clearPlots = widgets.Button(description="ClearPlots",layout=widgets.Layout(width='auto', grid_area='main'))
#display(buttonPlot2D)
text_field_spec = widgets.Text(value='Spectrum_number')
PlotOutput = widgets.Output(layout={'border': '1px solid black'})
TextOutput = widgets.Output(layout={'border': '1px solid black'})
list_widgets = [
widgets.VBox([workSpaceListWidget,buttonSelectWorkspace,TextOutput]),
widgets.VBox([buttonPlotSpectrum,text_field_spec]),
buttonPlot2D, widgets.VBox([clearPlots, PlotOutput])]
accordion = widgets.Accordion(children=list_widgets)
accordion.set_title(0, 'Select Data')
accordion.set_title(1, '1D Plot')
accordion.set_title(2, '2D Plot')
accordion.set_title(3, 'Plot Output')
display(accordion)
@PlotOutput.capture()
def on_plotSpectrum_button_clicked(a):
specNumber = int(text_field_spec.value)
print (type (specNumber),specNumber)
selectedWorkspace = workSpaceListWidget.value
dataY = mtd[selectedWorkspace].extractY()
plt.figure()
plt.plot(dataY[specNumber,:])
plt.show()
@PlotOutput.capture()
def on_plot2D_button_clicked(b):
selectedWorkspace = workSpaceListWidget.value
dataY = mtd[selectedWorkspace].extractY()
plt.figure()
plt.imshow(dataY,vmin=0,vmax=10,aspect='auto')
plt.show()
@PlotOutput.capture()
def on_clearPlots_button_clicked(c):
plt.close('all')
PlotOutput.clear_output()
@PlotOutput.capture()
def on_clear_clicked(d):
out.clear_output()
@TextOutput.capture()
def on_buttonSelectWorkspace_button_clicked(e):
selectedWorkspace = workSpaceListWidget.value
dataY = mtd[selectedWorkspace].extractY()
print (dataY.shape)
return dataY,selectedWorkspace
buttonPlotSpectrum.on_click(on_plotSpectrum_button_clicked)
buttonPlot2D.on_click(on_plot2D_button_clicked)
clearPlots.on_click(on_clearPlots_button_clicked)
def mantidWkspToHoloViewDataSet(wkspname):
import holoviews as hv
signal = mtd[wkspname].extractY()
error = mtd[wkspname].extractE()
TOF = mtd[wkspname].extractX()
wkspDim=signal.shape
dataCopy=np.zeros([wkspDim[0],wkspDim[1],2])
dataCopy[:,:,0]=signal
dataCopy[:,:,1]=error
ds = hv.Dataset((np.arange(wkspDim[1]),np.arange(wkspDim[0]),dataCopy[:,:,0]),['x', 'y'], 'Counts')
#ingore errors
return ds
import holoviews as hv
from holoviews import opts
hvDs = mantidWkspToHoloViewDataSet('w1')
points = hvDs.to(hv.Image)
points.opts(width=500, height=500)
This scaling issue of Z in 2D plots is common in visualistaion of scientific data. Most packages assume that the dynamic range of the data is low. When in this case (inelastic neutron scattering it is not)
We can uses the functionality of holoviews to make a data explorer widget.
import holoviews as hv
from holoviews import opts
hv.extension('bokeh')
opts.defaults(
opts.GridSpace(shared_xaxis=True, shared_yaxis=True),
opts.Image(cmap='jet', width=500, height=500),
opts.Labels(text_color='white', text_font_size='8pt', text_align='left', text_baseline='bottom'),
opts.Path(color='white'),
opts.Spread(width=600),
opts.Overlay(show_legend=False),
)
def superplot1(wksp):
hvDs = mantidWkspToHoloViewDataSet(wksp)
img = hvDs.to(hv.Image).opts(colorbar=True, colorbar_position = 'left')
posy = hv.streams.PointerY()
hline = hv.DynamicMap(lambda y: hv.HLine(y or -100), streams=[posy])
posx = hv.streams.PointerX()
vline = hv.DynamicMap(lambda x: hv.VLine(x or -100), streams=[posx])
crosssection2 = hv.DynamicMap(lambda y: img.sample(y=y if y else 0), streams=[posy])
crosssection1 = hv.DynamicMap(lambda x: img.sample(x=x if x else 0), streams=[posx])
# Combine images, vline and cross-sections
#((img1 * hline * vline) << crosssection1)+((img1 * vline) << crosssection1)
return ((img * hline * vline) << crosssection1 << crosssection2)
superplot1('w1')
import numpy as np
import holoviews as hv
from holoviews import opts
from holoviews import streams
from matplotlib import cm
hv.extension('bokeh')
from holoviews.operation.datashader import datashade
opts.defaults(
opts.GridSpace(shared_xaxis=True, shared_yaxis=True),
opts.Image(cmap='jet', width=700, height=700),
opts.Labels(text_color='white', text_font_size='8pt', text_align='left', text_baseline='bottom'),
opts.Path(color='white'),
opts.Spread(width=600),
opts.Overlay(show_legend=False),
)
ds = mantidWkspToHoloViewDataSet('w1')
#datashade(ds)
points = ds.to(hv.Image).opts(width=300, height=300)
shaded = datashade(points,cmap=cm.viridis).opts(width=300, height=300)
points + shaded
import numpy as np
import holoviews as hv
from holoviews import opts
from holoviews import streams
from holoviews.operation.datashader import datashade
from matplotlib import cm
hv.extension('bokeh')
opts.defaults(
opts.GridSpace(shared_xaxis=True, shared_yaxis=True),
opts.Image(cmap='jet', width=700, height=700),
opts.Labels(text_color='white', text_font_size='8pt', text_align='left', text_baseline='bottom'),
opts.Path(color='white'),
opts.Spread(width=600),
opts.Overlay(show_legend=False),
)
opts.defaults(opts.Histogram(framewise=True))
#ds = hv.Dataset((np.arange(2624),np.arange(922),dataCopy[:,:,0]),['x', 'y'], 'Counts')
#ds.data
#def holoViewInteractive2D(hvDataSet):
# Declare distribution of Points
def superplot2(wksp):
hvDs = mantidWkspToHoloViewDataSet(wksp)
points = hvDs.to(hv.Image)
points.opts(colorbar=True, colorbar_position = 'left')
# Declare a Bounds stream and DynamicMap to get box_select geometry and draw it
box = streams.BoundsXY(source=points, bounds=(0,0,0,0))
bounds = hv.DynamicMap(lambda bounds: hv.Bounds(bounds), streams=[box])
# Declare DynamicMap to apply bounds selection
dmap = hv.DynamicMap(lambda bounds: points.select(x=(bounds[0], bounds[2]),y=(bounds[1], bounds[3])),streams=[box])
# Compute histograms of selection along x-axis and y-axis
yhist = hv.operation.histogram(dmap, bin_range=points.range('y'), dimension='y', num_bins = 500, weight_dimension='Counts', mean_weighted=True)
xhist = hv.operation.histogram(dmap, bin_range=points.range('x'), dimension='x', num_bins = 500, weight_dimension='Counts', mean_weighted=True)
#hv.Curve(selection.aggregate('Time', np.mean))
#roi = ds.select(x=(bounds[0], bounds[2]),y=(bounds[1], bounds[3]), ).relabel('ROI')
#region = roi.to(hv.Image, ['x', 'y'])
# Combine components and display
#points * mean_sel * bounds << yhist << xhist
shaded = datashade(points,cmap=cm.viridis).opts(width=500, height=500)
return (shaded * bounds) << yhist << xhist
#holoViewInteractive2D(hvDs)
superplot2('w1')
The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible. To find out why, try turning on parallel diagnostics, see http://numba.pydata.org/numba-doc/latest/user/parallel.html#diagnostics for help. File "../../../lib/python3.6/site-packages/datashader/resampling.py", line 235: @ngjit_parallel def _get_dimensions(src, out): ^ invalid value encountered in true_divide
Qdata=SofQWNormalisedPolygon(InputWorkspace=w1,QAxisBinning='0,0.01,5',Emode='Direct',EFixed=12,ReplaceNaNs = True)
superplot2('Qdata')
invalid value encountered in true_divide
A python Package that acts as a abstraction layer between simulation codes and python
Lets use that to run a quick simulation of the Phonon modes in Aluminium.
For inelastic sccattering where ther signal is always small its a good idea to know what contributions can come from common materials found in the experiment like Aluminium
from ase.build import bulk
from ase.calculators.emt import EMT
from ase.phonons import Phonons
# Setup crystal and EMT calculator
atoms = bulk('Al', 'fcc', a=4.05)
# Phonon calculator
N = 7
ph = Phonons(atoms, EMT(), supercell=(N, N, N), delta=0.05)
ph.run()
# Read forces and assemble the dynamical matrix
ph.read(acoustic=True)
ph.clean()
path = atoms.cell.bandpath('GXULGK', npoints=100)
bs = ph.get_band_structure(path)
dos = ph.get_dos(kpts=(20, 20, 20)).sample_grid(npts=100, width=1e-3)
Writing phonon.eq.pckl Writing phonon.0x-.pckl Writing phonon.0x+.pckl Writing phonon.0y-.pckl Writing phonon.0y+.pckl Writing phonon.0z-.pckl Writing phonon.0z+.pckl
# Plot the band structure and DOS:
%matplotlib inline
import matplotlib.pyplot as plt
fig = plt.figure(1, figsize=(7, 4))
ax = fig.add_axes([.12, .07, .67, .85])
emax = 0.035
bs.plot(ax=ax, show=False, emin=0.0, emax=emax)
aa=bs.todict()
aa.keys
dosax = fig.add_axes([.8, .07, .17, .85])
dosax.fill_between(dos.weights[0], dos.energy, y2=0, color='grey',edgecolor='k', lw=1)
dosax.set_ylim(0, emax)
dosax.set_yticks([])
dosax.set_xticks([])
dosax.set_xlabel("DOS", fontsize=18)
Text(0.5, 0, 'DOS')
from ase.visualize import view
view(atoms,viewer='ngl')
import requests
import json
import urllib
def search_scicat(text, max_number_results):
fields = {'text': text}
limit = {'limit': max_number_results, 'order': "creationTime:desc"}
fields_encode = urllib.parse.quote(json.dumps(fields))
limit_encode = urllib.parse.quote(json.dumps(limit))
dataset_url = "https://scicatapi.esss.dk/api/v3/Datasets/anonymousquery?fields=" + \
fields_encode+"&limits="+limit_encode
r=requests.get(dataset_url).json()
print(len(r), "result found!")
return r
results=search_scicat("Al",100)
63 result found!
results
[{'owner': 'Clement Derrez',
'ownerEmail': 'Clement.Derrez@esss.se',
'contactEmail': 'Clement.Derrez@esss.se',
'sourceFolder': '/users/detector/experiments/beamInstrumentation/FMC-PICO-evaluation',
'size': 1792968,
'packedSize': 1792968,
'creationTime': '2019-03-29T10:02:27.000Z',
'type': 'raw',
'validationStatus': 'valid',
'keywords': ['BeamInstrumentation', 'neutron', 'detector'],
'description': 'This data was collected as part of the beam instrumentation program https://github.com/ess-dmsc/ess_file_formats/wiki/HDF5',
'datasetName': 'Sample Data from BeamInstrumentation 3',
'license': 'ESS',
'version': '3.01',
'isPublished': True,
'ownerGroup': 'ess',
'accessGroups': ['brightness', 'ess'],
'updatedBy': 'ingestor',
'updatedAt': '2019-04-30T11:51:21.959Z',
'datasetlifecycle': {'archivable': True,
'retrievable': False,
'dateOfDiskPurging': '2029-03-29T09:02:27.966Z',
'archiveRetentionTime': '2029-03-29T09:02:27.966Z',
'dateOfPublishing': '2022-03-29T09:02:27.966Z',
'archiveStatusMessage': 'Stored on primary disk and on tape',
'retrieveStatusMessage': 'string',
'archiveReturnMessage': {'string': 'string'},
'retrieveReturnMessage': {'string': 'string'},
'exportedTo': 'string'},
'principalInvestigator': 'Clement Derrez',
'creationLocation': 'BeamInstrumentation',
'scientificMetadata': {'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0/Channel_range': '0',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0/Sampling rate': '1000000',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0/Nb of points': '10000',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0/FMC SN': '18001',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0/FMC VERSION': 'FMC-Pico-1m4-C3',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0/Operator': 'Clement Derrez',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0/Location': 'BD lab room 4',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0/mTCA crate ID': '675',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0/Load': '100m AMGAB + IC',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0/Unit': 'A',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0/Data type': 'FLoat32',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0_OC/Channel_range': '0',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0_OC/Sampling rate': '1000000',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0_OC/Nb of points': '10000',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0_OC/FMC SN': '18001',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0_OC/FMC VERSION': 'FMC-Pico-1m4-C3',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0_OC/Operator': 'Clement Derrez',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0_OC/Location': 'BD lab room 4',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0_OC/mTCA crate ID': '675',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0_OC/Load': 'OC',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0_OC/Unit': 'A',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0_OC/Data type': 'FLoat32',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1/Channel_range': '1',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1/Sampling rate': '1000000',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1/Nb of points': '10000',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1/FMC SN': '18001',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1/FMC VERSION': 'FMC-Pico-1m4-C3',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1/Operator': 'Clement Derrez',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1/Location': 'BD lab room 4',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1/mTCA crate ID': '675',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1/Load': '100m AMGAB + IC',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1/Unit': 'A',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1/Data type': 'FLoat32',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1_OC/Channel_range': '1',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1_OC/Sampling rate': '1000000',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1_OC/Nb of points': '10000',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1_OC/FMC SN': '18001',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1_OC/FMC VERSION': 'FMC-Pico-1m4-C3',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1_OC/Operator': 'Clement Derrez',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1_OC/Location': 'BD lab room 4',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1_OC/mTCA crate ID': '675',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1_OC/Load': 'OC',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1_OC/Unit': 'A',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1_OC/Data type': 'FLoat32',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0/Channel_range': '0',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0/Sampling rate': '1000000',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0/Nb of points': '10000',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0/FMC SN': '18001',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0/FMC VERSION': 'FMC-Pico-1m4-C3',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0/Operator': 'Clement Derrez',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0/Location': 'BD lab room 4',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0/mTCA crate ID': '675',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0/Load': '100m AMGAB + IC',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0/Unit': 'A',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0/Data type': 'FLoat32',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0_OC/Channel_range': '0',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0_OC/Sampling rate': '1000000',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0_OC/Nb of points': '10000',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0_OC/FMC SN': '18001',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0_OC/FMC VERSION': 'FMC-Pico-1m4-C3',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0_OC/Operator': 'Clement Derrez',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0_OC/Location': 'BD lab room 4',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0_OC/mTCA crate ID': '675',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0_OC/Load': 'OC',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0_OC/Unit': 'A',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0_OC/Data type': 'FLoat32',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1/Channel_range': '1',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1/Sampling rate': '1000000',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1/Nb of points': '10000',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1/FMC SN': '18001',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1/FMC VERSION': 'FMC-Pico-1m4-C3',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1/Operator': 'Clement Derrez',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1/Location': 'BD lab room 4',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1/mTCA crate ID': '675',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1/Load': '100m AMGAB + IC',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1/Unit': 'A',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1/Data type': 'FLoat32',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1_OC/Channel_range': '1',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1_OC/Sampling rate': '1000000',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1_OC/Nb of points': '10000',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1_OC/FMC SN': '18001',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1_OC/FMC VERSION': 'FMC-Pico-1m4-C3',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1_OC/Operator': 'Clement Derrez',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1_OC/Location': 'BD lab room 4',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1_OC/mTCA crate ID': '675',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1_OC/Load': 'OC',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1_OC/Unit': 'A',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1_OC/Data type': 'FLoat32',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0/Channel_range': '0',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0/Sampling rate': '1000000',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0/Nb of points': '10000',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0/FMC SN': '18001',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0/FMC VERSION': 'FMC-Pico-1m4-C3',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0/Operator': 'Clement Derrez',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0/Location': 'BD lab room 4',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0/mTCA crate ID': '675',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0/Load': '100m AMGAB + IC',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0/Unit': 'A',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0/Data type': 'FLoat32',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0_OC/Channel_range': '0',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0_OC/Sampling rate': '1000000',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0_OC/Nb of points': '10000',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0_OC/FMC SN': '18001',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0_OC/FMC VERSION': 'FMC-Pico-1m4-C3',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0_OC/Operator': 'Clement Derrez',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0_OC/Location': 'BD lab room 4',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0_OC/mTCA crate ID': '675',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0_OC/Load': 'OC',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0_OC/Unit': 'A',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0_OC/Data type': 'FLoat32',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1/Channel_range': '1',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1/Sampling rate': '1000000',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1/Nb of points': '10000',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1/FMC SN': '18001',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1/FMC VERSION': 'FMC-Pico-1m4-C3',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1/Operator': 'Clement Derrez',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1/Location': 'BD lab room 4',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1/mTCA crate ID': '675',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1/Load': '100m AMGAB + IC',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1/Unit': 'A',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1/Data type': 'FLoat32',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1_OC/Channel_range': '1',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1_OC/Sampling rate': '1000000',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1_OC/Nb of points': '10000',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1_OC/FMC SN': '18001',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1_OC/FMC VERSION': 'FMC-Pico-1m4-C3',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1_OC/Operator': 'Clement Derrez',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1_OC/Location': 'BD lab room 4',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1_OC/mTCA crate ID': '675',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1_OC/Load': 'OC',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1_OC/Unit': 'A',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1_OC/Data type': 'FLoat32',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0/Channel_range': '0',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0/Sampling rate': '1000000',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0/Nb of points': '10000',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0/FMC SN': '18001',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0/FMC VERSION': 'FMC-Pico-1m4-C3',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0/Operator': 'Clement Derrez',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0/Location': 'BD lab room 4',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0/mTCA crate ID': '675',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0/Load': '100m AMGAB + IC',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0/Unit': 'A',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0/Data type': 'FLoat32',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0_OC/Channel_range': '0',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0_OC/Sampling rate': '1000000',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0_OC/Nb of points': '10000',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0_OC/FMC SN': '18001',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0_OC/FMC VERSION': 'FMC-Pico-1m4-C3',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0_OC/Operator': 'Clement Derrez',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0_OC/Location': 'BD lab room 4',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0_OC/mTCA crate ID': '675',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0_OC/Load': 'OC',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0_OC/Unit': 'A',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0_OC/Data type': 'FLoat32',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1/Channel_range': '1',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1/Sampling rate': '1000000',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1/Nb of points': '10000',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1/FMC SN': '18001',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1/FMC VERSION': 'FMC-Pico-1m4-C3',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1/Operator': 'Clement Derrez',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1/Location': 'BD lab room 4',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1/mTCA crate ID': '675',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1/Load': '100m AMGAB + IC',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1/Unit': 'A',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1/Data type': 'FLoat32',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1_OC/Channel_range': '1',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1_OC/Sampling rate': '1000000',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1_OC/Nb of points': '10000',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1_OC/FMC SN': '18001',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1_OC/FMC VERSION': 'FMC-Pico-1m4-C3',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1_OC/Operator': 'Clement Derrez',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1_OC/Location': 'BD lab room 4',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1_OC/mTCA crate ID': '675',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1_OC/Load': 'OC',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1_OC/Unit': 'A',
'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1_OC/Data type': 'FLoat32',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0/Channel_range': '0',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0/ion chamber SN': 'HCBLM I001-05005107',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0/Sampling rate': '1000000',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0/Nb of points': '10000',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0/FMC SN': '18002',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0/FMC VERSION': 'FMC-Pico-1m4-C3',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0/Operator': 'Clement Derrez',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0/Location': 'BD lab room 4',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0/mTCA crate ID': '675',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0/Load': '100m AMGAB + IC',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0/Unit': 'A',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0/Data type': 'FLoat32',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0_OC/Channel_range': '0',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0_OC/Sampling rate': '1000000',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0_OC/Nb of points': '10000',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0_OC/FMC SN': '18002',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0_OC/FMC VERSION': 'FMC-Pico-1m4-C3',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0_OC/Operator': 'Clement Derrez',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0_OC/Location': 'BD lab room 4',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0_OC/mTCA crate ID': '675',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0_OC/Load': 'OC',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0_OC/Unit': 'A',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0_OC/Data type': 'FLoat32',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1/Channel_range': '1',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1/ion chamber SN': 'HCBLM I001-05005107',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1/Sampling rate': '1000000',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1/Nb of points': '10000',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1/FMC SN': '18002',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1/FMC VERSION': 'FMC-Pico-1m4-C3',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1/Operator': 'Clement Derrez',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1/Location': 'BD lab room 4',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1/mTCA crate ID': '675',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1/Load': '100m AMGAB + IC',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1/Unit': 'A',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1/Data type': 'FLoat32',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1_OC/Channel_range': '1',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1_OC/Sampling rate': '1000000',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1_OC/Nb of points': '10000',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1_OC/FMC SN': '18002',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1_OC/FMC VERSION': 'FMC-Pico-1m4-C3',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1_OC/Operator': 'Clement Derrez',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1_OC/Location': 'BD lab room 4',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1_OC/mTCA crate ID': '675',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1_OC/Load': 'OC',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1_OC/Unit': 'A',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1_OC/Data type': 'FLoat32',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0/Channel_range': '0',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0/ion chamber SN': 'HCBLM I001-05005107',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0/Sampling rate': '1000000',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0/Nb of points': '10000',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0/FMC SN': '18002',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0/FMC VERSION': 'FMC-Pico-1m4-C3',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0/Operator': 'Clement Derrez',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0/Location': 'BD lab room 4',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0/mTCA crate ID': '675',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0/Load': '100m AMGAB + IC',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0/Unit': 'A',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0/Data type': 'FLoat32',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0_OC/Channel_range': '0',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0_OC/Sampling rate': '1000000',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0_OC/Nb of points': '10000',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0_OC/FMC SN': '18002',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0_OC/FMC VERSION': 'FMC-Pico-1m4-C3',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0_OC/Operator': 'Clement Derrez',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0_OC/Location': 'BD lab room 4',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0_OC/mTCA crate ID': '675',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0_OC/Load': 'OC',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0_OC/Unit': 'A',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0_OC/Data type': 'FLoat32',
'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG1/Channel_range': '1',
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print(results[0]["sourceFolder"])
/users/detector/experiments/beamInstrumentation/FMC-PICO-evaluation